Impact of Dietary Shifts on Gut Microbiome Dynamics

Multivariate Insights Using R

R for Bio Data Analysis

Group 16: Eric Torres, Lucia de Lamadrid, Konstantina Gkopi, Elena Iriondo and Jorge Santiago

2024-12-03

Introduction

Objective and Main Research Questions

Metagenomics study focusing on an animal model of the human gut ecosystem obtained by transplanting fresh human fecal microbial communities into germ-free C57BL/6J mice.
Measuring the effects of diet (Western or Low-Fat Plant Polysaccharide-rich), temporal and spatial composition of the microbiota, colonisation history…

Our aim:

To study the relationship between the composition of the gut microbiota and factors such as diet and colonisation history.

Main Research Questions:

Modelling of the Firmicutes-Bacteroidetes ratio
Deciphering key factors shaping ecosystem composition through PCA and clustering analysis
Evalauting the effect of diet in the microbial biodiversity

Data Loading

MicrobiomeWithMetadata.csv (Main dataset):
Contains the relative abundance of OTUs analyzed per observation, along with the conditions of each sample (e.g., diet, sex, etc.).

library(tidyverse) 
# Read the data
metadata_df <- read_tsv(file = str_c('../data/01_data_metadata.tsv'))

# Check the structure of the data
head(metadata_df)

# A tibble: 6 × 6,701
   Diet Source Donor CollectionMet   Sex     OTU0     OTU1     OTU2     OTU3
  <dbl>  <dbl> <dbl>         <dbl> <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
1     0      0     0             0     0 1.56e-11 4.72e-11 1.23e-11 4.52e-11
2     0      1     0             0     0 2.36e-11 9.53e-11 3.33e-11 2.67e-11
3     0      2     0             1     0 6.77e-11 3.68e-11 8.02e-11 5.49e-11
4     0      2     0             0     0 5.52e-11 9.89e-11 4.58e-11 3.54e-11
5     0      3     0             0     0 5.24e-11 6.34e-11 2.35e-11 7.47e-11
6     0      4     0             1     0 7.67e-11 7.22e-11 5.41e-11 1.20e-11
# ℹ 6,692 more variables: OTU4 <dbl>, OTU5 <dbl>, OTU6 <dbl>, OTU7 <dbl>,
#   OTU8 <dbl>, OTU9 <dbl>, OTU10 <dbl>, OTU11 <dbl>, OTU12 <dbl>, OTU13 <dbl>,
#   OTU14 <dbl>, OTU15 <dbl>, OTU16 <dbl>, OTU17 <dbl>, OTU18 <dbl>,
#   OTU19 <dbl>, OTU20 <dbl>, OTU21 <dbl>, OTU22 <dbl>, OTU23 <dbl>,
#   OTU24 <dbl>, OTU25 <dbl>, OTU26 <dbl>, OTU27 <dbl>, OTU28 <dbl>,
#   OTU29 <dbl>, OTU30 <dbl>, OTU31 <dbl>, OTU32 <dbl>, OTU33 <dbl>,
#   OTU34 <dbl>, OTU35 <dbl>, OTU36 <dbl>, OTU37 <dbl>, OTU38 <dbl>, …

MicrobiomeMetadataDictionary.csv (Complementary):
Provides the equivalence between coded numerical values and their corresponding sample conditions.
MicrobiomeOTUtaxonomy.csv (Complementary):
Contains the taxonomic classification of each OTU analyzed.
Source:
Explore Microbiome Dataset
Curated version of the microbiome dataset from Turnbaugh et al. (original publication available in accompanying files).

Materials and Methods

Data Tidying and Filtering

Added a SampleID column to uniquely identify each sample + Transformed the dataset from wide to long format for easier analysis.

# Creation and relocation of SampleID
metadata_df <- metadata_df |>
  mutate(SampleID = row_number()) |>  # Create SampleID from the first column
  relocate(SampleID, 
           .before = everything())  # Move SampleID to the first position

metadata_df_long <- metadata_df |> 
  pivot_longer(
    cols = starts_with("OTU"), 
    names_to = "OTU", 
    values_to = "rel_abundance"
  )
head(metadata_df_long)

# A tibble: 6 × 8
  SampleID  Diet Source Donor CollectionMet   Sex OTU   rel_abundance
     <int> <dbl>  <dbl> <dbl>         <dbl> <dbl> <chr>         <dbl>
1        1     0      0     0             0     0 OTU0       1.56e-11
2        1     0      0     0             0     0 OTU1       4.72e-11
3        1     0      0     0             0     0 OTU2       1.23e-11
4        1     0      0     0             0     0 OTU3       4.52e-11
5        1     0      0     0             0     0 OTU4       2.72e-11
6        1     0      0     0             0     0 OTU5       2.71e-11

Filtering Low-Abundance OTUs: Retained OTUs contributing up to 95% of cumulative abundance. Additionally, removed OTUs with extremely low relative abundances.
Replaced the numeric codes with descriptive labels.

# Calculate cumulative contribution
cumulative_otus <- metadata_df_long |>
  group_by(OTU) |>
  summarize(mean_abundance = mean(rel_abundance)) |>
  arrange(desc(mean_abundance)) |>
  mutate(cumulative_abundance = cumsum(mean_abundance) / sum(mean_abundance))
# Filter OTUs contributing to 95% cumulative abundance
otus_to_keep <- cumulative_otus |>
  filter(cumulative_abundance <= 0.95) |>
  pull(OTU)
# Filter the metadata to retain only these OTUs
filtered_metadata <- metadata_df_long |>
  filter(OTU %in% otus_to_keep)
dim(filtered_metadata)

[1] 264600      8

# Set the stricter abundance threshold
abundance_threshold <- 1e-6
# Apply the threshold to filter OTUs
filtered_metadata_stricter <- filtered_metadata |>
  filter(rel_abundance >= abundance_threshold)
dim(filtered_metadata_stricter)

[1] 65938     8

filtered_metadata_stricter_label <- filtered_metadata_stricter |> 
  mutate(Diet = case_when(Diet == 0 ~ "LFPP",
                          Diet == 1 ~ "Western",
                          Diet == 2 ~ "CARBR",
                          Diet == 3 ~ "FATR",
                          Diet == 4 ~ "Suckling",
                          Diet == 5 ~ "Human")) |> 
  mutate(Source = case_when(Source == 0 ~ "Cecum1",
                          Source == 1 ~ "Cecum2", 
                          Source == 2 ~ "Colon1", 
                          Source == 3 ~ "Colon2", 
                          Source == 4 ~ "Feces",
                          Source == 5 ~ "SI1",
                          Source == 6 ~ "SI13", 
                          Source == 7 ~ "SI15", 
                          Source == 8 ~ "SI2", 
                          Source == 9 ~ "SI5",
                          Source == 10 ~ "SI9", 
                          Source == 11 ~ "Stomach", 
                          Source == 12 ~ "Cecum")) |> 
  mutate(Donor = case_when(Donor == 0 ~ "HMouseLFPP",
                          Donor == 1 ~ "CONVR", 
                          Donor == 2 ~ "Human", 
                          Donor == 3 ~ "Fresh", 
                          Donor == 4 ~ "Frozen",
                          Donor == 5 ~ "HMouseWestern", 
                          Donor == 6 ~ "CONVD")) |> 
  mutate(CollectionMet = case_when(CollectionMet == 0 ~ "Contents",
                                   CollectionMet == 1 ~ "Scraping")) |> 
  mutate(Sex = case_when(Sex == 0 ~ "Male",
                         Sex == 1 ~ "Female"))

Our data is tidy… and ready to be augmented!

We will use the OTUs taxonomy file to add columns with the names of phylum and class for each OTU, using left_join.

clean_df <- read_tsv('../data/02_metadata_long_filtered_label.tsv')
head(clean_df)

# A tibble: 6 × 9
   ...1 SampleID Diet  Source Donor      CollectionMet Sex   OTU   rel_abundance
  <dbl>    <dbl> <chr> <chr>  <chr>      <chr>         <chr> <chr>         <dbl>
1     1        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU9       0.00257 
2     2        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU77      0.00128 
3     3        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU1…      0.00449 
4     4        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU1…      0.000641
5     5        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU3…      0.000641
6     6        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU3…      0.00257

otu_df_original <- read.table('../data/01_data_otu.tsv', header = TRUE, sep = ",")
head(otu_df_original)

  OTU.ID  Kingdom        Phylum         Class           Order
1   OTU0 Bacteria                                            
2   OTU1 Bacteria    Firmicutes    Clostridia   Clostridiales
3   OTU2 Bacteria    Firmicutes       Bacilli Lactobacillales
4   OTU3 Bacteria Bacteroidetes Bacteroidetes   Bacteroidales
5   OTU4 Bacteria Bacteroidetes                              
6   OTU5 Bacteria    Firmicutes    Clostridia   Clostridiales
              Family           Genus X X.1
1                                         
2    Ruminococcaceae                      
3    Enterococcaceae    Enterococcus      
4 Porphyromonadaceae Parabacteroides      
5                                         
6

clean_df_augm <- clean_df |>  
  left_join(otu_df_modified, 
            join_by(OTU == OTU.ID)) |> 
  relocate(Phylum, Class, .after = OTU) 
head(clean_df_augm)

# A tibble: 6 × 11
   ...1 SampleID Diet  Source Donor      CollectionMet Sex   OTU    Phylum Class
  <dbl>    <dbl> <chr> <chr>  <chr>      <chr>         <chr> <chr>  <chr>  <chr>
1     1        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU9   Firmi… Clos…
2     2        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU77  Firmi… Clos…
3     3        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU155 Firmi… Clos…
4     4        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU170 Firmi… Clos…
5     5        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU336 Firmi… Clos…
6     6        1 LFPP  Cecum1 HMouseLFPP Contents      Male  OTU370 Bacte… Bact…
# ℹ 1 more variable: rel_abundance <dbl>

Results

Microbiota composition in terms of phyla in different:

sources and diet types

diet and donor combination

05 Principal Component Analysis on Phylum-Level Aggregated Microbiome Data

# Aggregate relative abundances by phylum
aggregated_data <- clean_df_augm |>
  filter(Donor == "Fresh") |> #we select the first generation of humanised mice (we don't select the western and lfpp diet as these are the only diets these mice follow)
  group_by(SampleID, Phylum, Diet) |> 
  summarize(rel_abundance = sum(rel_abundance), .groups = "drop")

# Pivot wider and one-hot coding of diet variable to prepare for PCA (SampleID by Phylum)
aggregated_wide <- aggregated_data |> 
  pivot_wider(names_from = Phylum, values_from = rel_abundance) |> 
  mutate(Diet = case_when(Diet == "LFPP" ~ 0, Diet == "Western" ~ 1)) #pca uses numerical values so we modify the diet column

# Check of the aggregated data
head(aggregated_wide)

# A tibble: 6 × 8
  SampleID  Diet Actinobacteria Bacteroidetes Firmicutes Proteobacteria
     <dbl> <dbl>          <dbl>         <dbl>      <dbl>          <dbl>
1      339     1        0.00316        0.463       0.470        0.00671
2      340     1        0.0180         0.106       0.811        0.00138
3      341     1        0.0121         0.0443      0.873        0.00792
4      342     1        0.00531        0.145       0.786        0.0135 
5      343     1        0.00801        0.607       0.300        0.0300 
6      344     1        0.631          0.199       0.141        0.00467
# ℹ 2 more variables: Unclassified <dbl>, Verrucomicrobia <dbl>

Principal Component Analysis on Phylum-Level Aggregated Microbiome Data

Analysis of Microbiome Clusters by Donor Groups Using Hierarchical Clustering

# Read the metadata in wide format
filtered_metadata_wider <- read_tsv("../data/02_metadata_wide_filtered_label.tsv")
# Select OTU columns
otu_data <- filtered_metadata_wider |>
  select(starts_with("OTU"))

# Scale the OTU data
otu_data_scaled <- otu_data |> 
  scale()

# Convert scaled matrix back to tibble for tidyverse compatibility
otu_data_scaled <- as_tibble(otu_data_scaled)

# Add relevant metadata (e.g., Donor)
otu_data_with_metadata <- otu_data_scaled |>
  mutate(Donor = filtered_metadata_wider |> pull(Donor))

# Compute Euclidean distance matrix
dist_matrix <- otu_data_scaled |>
  dist()

# Perform hierarchical clustering
hclust_result <- hclust(dist_matrix, method = "ward.D2")

# Cut dendrogram into 3 clusters
cluster_labels <- cutree(hclust_result, k = 3) |>
  as_tibble() |>
  rename(Cluster = value)

# Attach cluster labels to metadata
clustered_metadata <- filtered_metadata_wider |> 
  mutate(Cluster = cluster_labels |> pull(Cluster))

07 Discussion

The “Obesity-inducing” diet influences the Firmicutes-Bacteroidetes ratio

PCA shows how diet shapes microbial composition, as well as the relationship between different phyla.
Clustering shines light on how the microbiota donor structures the data
The Western diet favours a more biodiverse gut ecosystem